U.S. Treasury yields are central to global asset pricing but are noisy and subject to policy uncertainty, supply-demand forces, and behavioral effects, exposing forecast users to downside risk. We formulate yield curve forecasting as a decision problem under distributional uncertainty and propose a distributionally robust ensemble framework that combines parametric factor models with machine-learning forecasts. A factor-augmented Dynamic Nelson-Siegel model captures yield-curve dynamics, while Random Forests model nonlinear interactions. Robust forecast combinations penalize tail risk and improve out-of-sample performance across maturities. The framework supports disciplined $DV01$-based interest-rate risk management for corporate, institutional and balance-sheet decision makers.

翻译：美国国债收益率是全球资产定价的核心要素，但其噪声高、受政策不确定性、供需力量及行为效应影响，致使预测使用者面临下行风险。本文将收益率曲线预测建模为分布不确定性下的决策问题，提出一种融合参数因子模型与机器学习预测的分布鲁棒集成框架。其中，因子增强型动态Nelson-Siegel模型捕捉收益率曲线动态，随机森林模型刻画非线性交互作用。鲁棒预测组合方法对尾部风险进行惩罚，并提升各期限样本外预测表现。该框架为企业和机构及资产负债决策者提供了基于$DV01$的规范化利率风险管理支持。